universityhack2018 0.3.0

Package for Data Rhapsody's UniversityHack 2018 Challenge solution.

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Data Rhapsody UniversityHack2018
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:target: https://pypi.python.org/pypi/universityhack2018

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:target: https://travis-ci.org/cabadsanchez/universityhack2018

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:target: https://universityhack2018.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status




Package for Data Rhapsody's UniversityHack 2018 Challenge solution.


* Free software: MIT license
* Documentation: https://universityhack2018.readthedocs.io.


Usage
-----

.. code-block:: python
from universityhack2018.feature_engineering import FeatureEngineering
from universityhack2018.prediction import Model
import pandas as pd

clients_df = pd.read_csv('/path/to/Dataset_Salesforce_Predictive_Modelling_TEST.txt')
clients = client_df_train.iloc[0:5, :]

model = Model(clients)
predictions = model.predict(as_df=True)

print(predictions.head())

# Output:
# ID_Customer PA_Est
# 0 TE000001 26926.541016
# 1 TE000002 15267.800781
# 2 TE000003 19499.935547
# 3 TE000004 12799.532227
# 4 TE000005 11262.253906

Features
--------

* TODO

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


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History
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0.1.0 (2018-03-11)
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* First release on PyPI.